
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import sqlite3
import requests

from PIL import Image

import warnings
warnings.filterwarnings('ignore')

%matplotlib inline


db_loc = 'data/bgg_data.sqlite'

def query_db(statement):
    '''
    Run the given query against the local Sqlite database file, and return the results in a DataFrame.
    '''
    
    conn = sqlite3.connect(db_loc)
    try:
        df = pd.read_sql(statement, conn)
    finally:
        conn.close()
    return df


def plot_category_bar(df, col_name, n, title_col, figsize=(12, 8)):
    '''
    For the given DataFrame, plot a bar chart counting all instances for the given column,
    then plot a similar bar chart for the column for each name in the `person` column.
    '''
    
    df_count = df.groupby(col_name).size().reset_index(name='count').nlargest(n, 'count')
    labels = df[col_name].unique()

    # assign a color to each label value for consistency
    palette = dict(zip(labels, sns.color_palette(n_colors=len(labels))))

    # plot the chart for the total distribution
    f = plt.figure(1, figsize=figsize)
    f.suptitle(f'Total {title_col} distribution')
    sns.barplot(data=df_count, x=col_name, y='count', palette=palette)
    plt.xticks(rotation=90)

    users = df['person'].unique()

    df_count = df.groupby(['person', col_name]).size().reset_index(name='count')

    # we can't guarantee that the labels match for each user
    # therefore, plot a new chart for each user
    fig_count = 2
    for user in users:
        f = plt.figure(fig_count, figsize=figsize)
        user_count = df_count[df_count['person'] == user].nlargest(n, 'count')
        sns.barplot(data=user_count, x=col_name, y='count', palette=palette)
        plt.xticks(rotation=90)
        f.suptitle(f'{user} {title_col} distribution')
        fig_count += 1
    plt.show()


def download_image(url):
    '''Download the image from the given URL, then return it in an Image object.'''
    r = requests.get(url, stream=True)
    im = Image.open(r.raw)
    return im


query_db(
"""
select *
from user_ranks
inner join games
on user_ranks.game_id = games.id
where user_ranks.name =='Jess'
""")


user_ranks = query_db(
"""
select user_ranks.name as person, user_ranks.rank, games.*
from user_ranks
inner join games
    on user_ranks.game_id = games.id
""")

games = query_db('select * from games')


top10 = user_ranks[user_ranks['rank'] <= 10]

users = list(top10['person'].unique())
cols = len(users) + 1
rows = 10 + 1

gridspec = {
    'width_ratios': [1] + ([5] * len(users)), # [1, 5, 5, ...] one 5 per user
    'height_ratios': [1] + ([3] * (rows-1)), # [1, 5, 5, ...] one 5 per 50 games,
    'wspace': 0.01
}
fig, axes = plt.subplots(rows, cols, figsize=(20, 15), gridspec_kw=gridspec)
fig.tight_layout(pad=10.0)
plt.subplots_adjust(top=5)

# top left corner
axes[0, 0].axis('off')

# header row
row_idx = 1
for user in users:
    ax = axes[0, row_idx]
    ax.set_title(user)
    ax.axis('off')
    row_idx += 1

# top 50 list rows
for rank, group in top10.groupby('rank'):
    # ranking number display
    ax = axes[rank, 0]
    ax.set_title(f'#{rank}')
    ax.axis('off')
    
    # games per user
    row_idx = 1
    for user in users:
        row = group[group['person'] == user]
        
        url = row['img_url'].item()
        title = row['name'].item()
        title = (title[:25] + '...') if len(title) > 25 else title
        img = download_image(url)
        
        ax = axes[rank, row_idx]
        ax.imshow(img)
        ax.set_title(title)
        ax.axis('off')
        
        row_idx += 1
        
plt.show()


user_ranks[['person', 'name', 'rank']].pivot(index='rank', columns='person')


# find every game with more than one person having it in their list
# join the users with commas
cross = user_ranks[['person', 'id']].groupby('id')['person'].apply(','.join).reset_index(name='people')

# get the count by counting the commas + 1 (e.g. "Person1,Person2" -> 2)
cross['count'] = cross['people'].str.count(',') + 1

# filter games to include only ones with crossover
cross = cross[cross['count'] > 1].sort_values('count', ascending=False)

# join with the games DataFrame to get the name
cross = pd.merge(cross, games, how='inner', on=['id', 'id'])

# display the images for each group
# todo: order this by number in the group (i.e. show crossover between group before pair)
for users, group in cross.groupby('people'):
    users = users.split(',')
    print('-' * 100)
    print('Crossovers exclusive to %s' % ' & '.join(users))
    
    urls = list(group['img_url'])
    titles = list(group['name'])

    # set width / height for the image grid
    # note: a maximum of 25 images will be shown (not anticipating more than that anyway)
    width = 5
    height = 5
    axes = []
    fig = plt.figure(figsize=(16,16))
    
    for a in range(min(len(urls), width * height)):
        axes.append(fig.add_subplot(height, width, a+1))
        url = urls[a]
        title = titles[a]
        img = download_image(url)
        plt.imshow(img)
        axes[-1].set_title(title)
        plt.axis('off')
    fig.tight_layout()
    plt.show()

----------------------------------------------------------------------------------------------------
Crossovers exclusive to Darin & Jess

----------------------------------------------------------------------------------------------------
Crossovers exclusive to Darin & Jess & Seth

----------------------------------------------------------------------------------------------------
Crossovers exclusive to Darin & Jess & Seth & Zarin

----------------------------------------------------------------------------------------------------
Crossovers exclusive to Darin & Seth & Zarin

----------------------------------------------------------------------------------------------------
Crossovers exclusive to Darin & Zarin

----------------------------------------------------------------------------------------------------
Crossovers exclusive to Jess & Seth

----------------------------------------------------------------------------------------------------
Crossovers exclusive to Jess & Seth & Zarin

----------------------------------------------------------------------------------------------------
Crossovers exclusive to Jess & Zarin

----------------------------------------------------------------------------------------------------
Crossovers exclusive to Seth & Zarin


user_ranks = query_db(
"""
select user_ranks.name as person, user_ranks.rank, games.*
from user_ranks
inner join games
    on user_ranks.game_id = games.id
""")

user_ranks['bgg_rank'] = user_ranks['bgg_rank'].astype(int)


MAX_BGG_RANK = 2000

rankings_in_range = user_ranks[user_ranks['bgg_rank'] < MAX_BGG_RANK]
over_2000 = user_ranks[user_ranks['bgg_rank'] >= MAX_BGG_RANK]

if not over_2000.empty:
    print('Ignoring %d element(s) with BGG rank > 2000:' % len(over_2000))
    print(over_2000[['person', 'name', 'bgg_rank']])

Ignoring 6 element(s) with BGG rank > 2000:
    person          name  bgg_rank
22   Darin         Haven      2391
24   Darin         Nertz      5726
25   Darin  Bezzerwizzer      2504
148   Seth   Catchphrase      9256
185  Zarin        Kenjin      5416
186  Zarin    Dice Miner      3032


sns.lmplot(x='rank', y='bgg_rank', data=rankings_in_range, col='person')
plt.suptitle("How do each user's rankings match against BGG?")
plt.tight_layout()
plt.show()


plt.figure(figsize=(16, 9))
plt.title('BGG rank distribution per user')
sns.boxplot(x='person', y='bgg_rank', data=rankings_in_range, boxprops=dict(alpha=0.6))
sns.swarmplot(x='person', y='bgg_rank', data=rankings_in_range, color='0.25')
plt.ylabel('BGG Rank')
plt.xlabel('')
plt.show()


top_100 = user_ranks[['person', 'bgg_rank']]
top_100['rank'] = pd.cut(top_100['bgg_rank'], bins=[0, 100, 500, np.inf], labels=['Top 100', '101-500', '500+'])
top_100 = top_100.groupby(['person', 'rank']).size().unstack(fill_value=0)

labels = top_100.columns

# pull out the pie portion for top 100
explode = [0.1 if col == 'Top 100' else 0 for col in labels]

fig, axes = plt.subplots(2, len(top_100.index), figsize=(12, 9))
fig.tight_layout()

# zero out upper row
for i in range(len(top_100.index)):
    axes[0, i].axis('off')

# set upper left corner to show total distribution
axes[0, 0].set_title('Total ranking distribution')
axes[0, 0].pie(top_100.sum(), labels=labels, explode=explode, autopct='%1.1f%%', startangle=90, shadow=True)

# set second row to show individual distributions
fig_count = 0
for i in top_100.index:
    axes[1, fig_count].set_title(f'{i} ranking distribution')
    axes[1, fig_count].pie(top_100.loc[i], labels=labels, explode=explode, autopct='%1.1f%%', startangle=90, shadow=True)
    
    fig_count += 1


plt.figure(figsize=(16, 9))
plt.title('Weight distribution per user')
ax = sns.violinplot(x='person', y='weight', data=user_ranks, inner='quartile', scale='count', palette='muted')
sns.swarmplot(x='person', y='weight', data=user_ranks, color='0.25')
plt.ylabel('Weight')
plt.xlabel('')
plt.show()


plt.figure(figsize=(16, 16))
counts_by_user = user_ranks.groupby(['person', 'best_count']).size().reset_index(name='count')
sns.catplot(data=counts_by_user, kind='bar', x='best_count', y='count', hue='person', alpha=0.9, height=12)
plt.title('Players counts by person')
plt.xlabel('Player count')
plt.ylabel('Total')
plt.show()

<Figure size 1152x1152 with 0 Axes>


MIN_YEAR = 1990
MAX_YEAR = 2021

users = user_ranks['person'].unique()

fig, axes = plt.subplots(nrows=len(users), ncols=1, figsize=(16, 25))

index = 0

for user in users:
    # get the games for this person, group them by year and count them
    by_year = user_ranks[user_ranks['person'] == user].groupby('year').size().reset_index(name='count')
    
    # do we have any that are below the minimum year? 
    # if so, print them out with a nice little note
    below_limit = by_year[by_year['year'] < MIN_YEAR]['year'].unique()
    if below_limit.any():
        print(f'Note - not including games for {user} from the following years for readability:')
        for year in below_limit:
            print(f' - {year}')
           
    # filter to recent years to make the graph readable
    by_year = by_year[by_year['year'] >= MIN_YEAR]
    
    # add values for missing years to spread out the bars
    for year in range(MIN_YEAR, MAX_YEAR+1):
        if year not in by_year['year'].values:
            by_year = by_year.append({'year': year, 'count': 0}, ignore_index=True)
    
    barplot = sns.barplot(data=by_year, x='year', y='count', ax=axes[index])
    barplot.set_xticklabels(barplot.get_xticklabels(), rotation=90, horizontalalignment='center')
    axes[index].set_title(f"{user}'s top 50 games by year")
    axes[index].set_ylabel('Total')
    axes[index].set_xlabel('Release date')
    
    index += 1
plt.tight_layout()
plt.show()

Note - not including games for Darin from the following years for readability:
 - 1630
 - 1930
Note - not including games for Jess from the following years for readability:
 - 1800
 - 1980
Note - not including games for Seth from the following years for readability:
 - 1974
 - 1980


low_counts = user_ranks.sort_values('users_rated', ascending=True)
low_counts = low_counts[['person', 'name', 'year', 'users_rated']]
low_counts.head(10)


plt.figure(figsize=(16, 9))
plt.title('User ratings distribution per user')
ax = sns.boxplot(x='person', y='users_rated', data=user_ranks, palette='muted')
sns.swarmplot(x='person', y='users_rated', data=user_ranks, color='0.25')
plt.ylabel('Users Rated')
plt.xlabel('')
plt.show()


mechanics = query_db(
"""
select user_ranks.name as person, games.name, mechanics.mechanic
from user_ranks
inner join games
    on user_ranks.game_id = games.id
inner join mechanics
    on games.id = mechanics.game_id
""")


plot_category_bar(mechanics, 'mechanic', title_col='mechanic', n=10)


families = query_db(
"""
select user_ranks.name as person, games.name, families.family
from user_ranks
inner join games
    on user_ranks.game_id = games.id
inner join families
    on games.id = families.game_id
""")


kickstarter_count = families.groupby(['person', 'family']).size().reset_index(name='count')
kickstarter_count = kickstarter_count[kickstarter_count['family'].str.contains('Kickstarter')]
sns.barplot(x='person', y='count', data=kickstarter_count)
plt.title('Which person has the most Kickstarter games?')
plt.show()


component_family = families[families['family'].str.startswith('Components')]

# remove 'Components: ' from the start of the family name
component_family['family'] = component_family['family'].str.replace('Components: ', '')
plot_category_bar(component_family, col_name='family', n=5, title_col='Component family', figsize=(6, 6))


categories = query_db(
"""
select user_ranks.name as person, games.name, categories.category
from user_ranks
inner join games
    on user_ranks.game_id = games.id
inner join categories
    on games.id = categories.game_id
""")


plot_category_bar(categories, col_name='category', n=10, title_col='Game Category', figsize=(6, 6))

	name	game_id	rank	id	name	year	playing_time	rating	weight	bgg_rank	best_count	min_count	max_count	expansions	users_rated	img_url
0	Jess	28143	1	28143	Race for the Galaxy	2007	60	7.75687	2.9882	67	2	2	4	8	49463	https://cf.geekdo-images.com/-DOqixs8uwKUvvWPK...
1	Jess	124361	2	124361	Concordia	2013	100	8.12226	3.0098	19	4	2	5	8	32258	https://cf.geekdo-images.com/CzwSm8i7tkLz6cBnr...
2	Jess	84876	3	84876	The Castles of Burgundy	2011	90	8.12637	2.9977	15	2	2	4	14	51485	https://cf.geekdo-images.com/5CFwjd8zTcGYVUnkX...
3	Jess	21241	4	21241	Neuroshima Hex! 3.0	2006	30	7.39981	2.6157	304	2	1	4	35	13927	https://cf.geekdo-images.com/8KB-cVXjNmYA6xuw-...
4	Jess	215	5	215	Tichu	1991	60	7.57592	2.3391	191	4	4	4	2	14009	https://cf.geekdo-images.com/gz8_8iYP2SSGVAxpp...
5	Jess	31260	6	31260	Agricola	2007	150	7.92074	3.6395	39	4	1	5	32	66685	https://cf.geekdo-images.com/dDDo2Hexl80ucK1Il...
6	Jess	233078	7	233078	Twilight Imperium: Fourth Edition	2017	480	8.66749	4.2581	6	6	3	6	3	16587	https://cf.geekdo-images.com/_Ppn5lssO5OaildSE...
7	Jess	146652	8	146652	Legendary Encounters: An Alien Deck Building Game	2014	60	7.77406	2.7054	127	3	1	5	3	12101	https://cf.geekdo-images.com/jSz_KRUxsjGYitoqx...
8	Jess	48726	9	48726	Alien Frontiers	2010	90	7.39297	2.5694	269	3	2	4	19	14570	https://cf.geekdo-images.com/UWZTWLXFfg_6HpXJm...
9	Jess	41	10	41	Can't Stop	1980	30	6.85736	1.1542	755	3	2	4	1	14817	https://cf.geekdo-images.com/JZpo3AnyFH4TqxKWF...
10	Jess	30549	11	30549	Pandemic	2008	45	7.58384	2.4058	110	4	2	4	7	110450	https://cf.geekdo-images.com/S3ybV1LAp-8SnHIXL...
11	Jess	167791	12	167791	Terraforming Mars	2016	120	8.41486	3.2462	4	3	1	5	23	76364	https://cf.geekdo-images.com/wg9oOLcsKvDesSUdZ...
12	Jess	2655	13	2655	Hive	2000	20	7.32242	2.3223	262	2	2	2	22	32195	https://cf.geekdo-images.com/fQe85tsBZoH6ibPnm...
13	Jess	822	14	822	Carcassonne	2000	45	7.41729	1.9047	197	2	2	5	160	110291	https://cf.geekdo-images.com/okM0dq_bEXnbyQTOv...
14	Jess	153938	15	153938	Camel Up	2014	30	7.04189	1.4759	510	5	2	8	4	24289	https://cf.geekdo-images.com/1ph2jVOD1MudR1fK1...
15	Jess	172	16	172	For Sale	1997	30	7.22917	1.2588	320	5	3	6	2	26120	https://cf.geekdo-images.com/dJh9HkZC346NgPTAi...
16	Jess	10630	17	10630	Memoir '44	2004	60	7.55588	2.2712	154	2	2	8	32	27104	https://cf.geekdo-images.com/chfLNSL0l7afCA5go...
17	Jess	96848	18	96848	Mage Knight Board Game	2011	240	8.09556	4.3309	29	1	1	4	4	29748	https://cf.geekdo-images.com/DUO2hz9AlLOH8p9ED...
18	Jess	39463	19	39463	Cosmic Encounter	2008	120	7.53787	2.5772	165	5	3	5	7	29163	https://cf.geekdo-images.com/S8cE-Ld7XP5sVz-up...
19	Jess	265188	20	265188	Glen More II: Chronicles	2019	120	7.98949	3.0583	166	3	2	4	4	5367	https://cf.geekdo-images.com/sMRuph3HFUcaSpJZi...
20	Jess	98778	21	98778	Hanabi	2010	25	7.06822	1.6939	432	4	2	5	4	42205	https://cf.geekdo-images.com/JDVksMwfcqoem1k_x...
21	Jess	161936	22	161936	Pandemic Legacy: Season 1	2015	60	8.58862	2.8305	2	4	2	4	0	45806	https://cf.geekdo-images.com/-Qer2BBPG7qGGDu6K...
22	Jess	2453	23	2453	Blokus	2000	20	6.87211	1.7612	699	4	2	4	0	24381	https://cf.geekdo-images.com/96YA5wUJDxtPkmPxe...
23	Jess	244521	24	244521	The Quacks of Quedlinburg	2018	45	7.85208	1.9506	57	4	2	4	5	31480	https://cf.geekdo-images.com/pH5LgRL4mNRon-2Ns...
24	Jess	8217	25	8217	San Juan	2004	60	7.24649	2.2863	325	3	2	4	2	22812	https://cf.geekdo-images.com/kiD03bslLpN_0Qe1L...
25	Jess	233867	26	233867	Welcome To...	2018	25	7.59263	1.8242	138	4	1	100	14	22494	https://cf.geekdo-images.com/g4XmxyKhNVdhC3QPd...
26	Jess	128882	27	128882	The Resistance: Avalon	2012	30	7.55872	1.7645	156	7	5	10	2	29204	https://cf.geekdo-images.com/LPa6rsGcv8S0-OeNj...
27	Jess	37380	28	37380	Roll Through the Ages: The Bronze Age	2008	45	6.86454	1.7807	785	2	1	4	1	11726	https://cf.geekdo-images.com/VoLyy7ScO6aAU9Neo...
28	Jess	183394	29	183394	Viticulture Essential Edition	2015	90	8.05300	2.8898	25	4	1	6	6	36038	https://cf.geekdo-images.com/l_PRU2lVlX9seScRF...
29	Jess	262543	30	262543	Wavelength	2019	45	7.42383	1.1193	405	6	2	12	1	6901	https://cf.geekdo-images.com/z4fbPdmJg_5yphJEv...
30	Jess	157969	31	157969	Sheriff of Nottingham	2014	60	7.11653	1.6529	412	5	3	5	16	31896	https://cf.geekdo-images.com/BBgLFKUzr6tcKtlIM...
31	Jess	92415	32	92415	Skull	2011	45	7.20158	1.1304	400	5	3	6	1	17072	https://cf.geekdo-images.com/OPrd2iXm43dir7BwK...
32	Jess	98351	33	98351	Core Worlds	2011	120	7.17473	2.8941	805	3	2	5	9	4084	https://cf.geekdo-images.com/Yim5NN5RexitX1ZaA...
33	Jess	36218	34	36218	Dominion	2008	30	7.60768	2.3542	106	3	2	4	48	82410	https://cf.geekdo-images.com/j6iQpZ4XkemZP07HN...
34	Jess	34635	35	34635	Stone Age	2008	90	7.55154	2.4701	130	4	2	4	7	47790	https://cf.geekdo-images.com/elmZegVZ6gp4_5izU...
35	Jess	70323	36	70323	King of Tokyo	2011	30	7.16593	1.4902	350	4	2	6	17	62106	https://cf.geekdo-images.com/m_RzXpHURC0_xLkvR...
36	Jess	42215	37	42215	Tobago	2009	60	7.10627	2.1281	532	4	2	4	1	11236	https://cf.geekdo-images.com/fuC2C10_6QK84R45M...
37	Jess	12942	38	12942	No Thanks!	2004	20	7.05878	1.1362	473	5	3	7	2	22310	https://cf.geekdo-images.com/2DyxMVASynw87LRUL...
38	Jess	50	39	50	Lost Cities	1999	30	7.19520	1.4901	329	2	2	2	2	40109	https://cf.geekdo-images.com/AL5D-dXabY-Lk3PqI...
39	Jess	254640	40	254640	Just One	2018	20	7.61912	1.0450	146	6	3	7	5	16771	https://cf.geekdo-images.com/qKcKjHpzAvLddwBhy...
40	Jess	203420	41	203420	Exit: The Game – The Abandoned Cabin	2016	120	7.49118	2.5461	267	2	1	6	0	9908	https://cf.geekdo-images.com/r7cZVZMpP28w3-JQ-...
41	Jess	160477	42	160477	Onitama	2014	20	7.38294	1.6807	270	2	2	2	9	17724	https://cf.geekdo-images.com/FZdihu8SibqQYtwfo...
42	Jess	163412	43	163412	Patchwork	2014	30	7.63233	1.6213	96	2	2	2	3	53518	https://cf.geekdo-images.com/wLW7pFn0--20lEizE...
43	Jess	152162	44	152162	Diamonds: Second Edition	2014	30	6.92018	1.5471	975	4	2	6	1	5129	https://cf.geekdo-images.com/jWa2swVQ1oJ_lmBjO...
44	Jess	68448	45	68448	7 Wonders	2010	30	7.72904	2.3246	75	4	2	7	16	91329	https://cf.geekdo-images.com/RvFVTEpnbb4NM7k0I...
45	Jess	171668	46	171668	The Grizzled	2015	30	7.17220	1.9538	459	4	2	5	1	13238	https://cf.geekdo-images.com/5JuQCtSAeSZpAL3Wg...
46	Jess	2651	47	2651	Power Grid	2004	120	7.83964	3.2652	49	4	2	6	31	59184	https://cf.geekdo-images.com/yd6LuatytHRhcFCxC...
47	Jess	73761	48	73761	K2	2010	60	7.03953	2.2598	637	3	1	5	3	9678	https://cf.geekdo-images.com/JlUNpGfe_k7FSYLTr...
48	Jess	760	49	760	Battle Line	2000	30	7.41472	1.8992	246	2	2	2	0	16290	https://cf.geekdo-images.com/RnbZfspkPRfr6EMkR...
49	Jess	45	50	45	Perudo	1800	30	6.90518	1.2621	733	6	2	6	0	13362	https://cf.geekdo-images.com/UCiGSoCqbOsXS8JzZ...

	name
person	Darin	Jess	Seth	Zarin
rank
1	The Castles of Burgundy	Race for the Galaxy	Terra Mystica	Terra Mystica
2	7 Wonders Duel	Concordia	Magic: The Gathering	Dune: Imperium
3	Fury of Dracula (Third/Fourth Edition)	The Castles of Burgundy	Tichu	Lost Ruins of Arnak
4	Tzolk'in: The Mayan Calendar	Neuroshima Hex! 3.0	Lorenzo il Magnifico	Terraforming Mars
5	Viticulture Essential Edition	Tichu	Lost Ruins of Arnak	Brass: Birmingham
6	Gloomhaven	Agricola	The Quacks of Quedlinburg	Great Western Trail
7	Clank!: Legacy – Acquisitions Incorporated	Twilight Imperium: Fourth Edition	Everdell	Dominion
8	Targi	Legendary Encounters: An Alien Deck Building Game	Tzolk'in: The Mayan Calendar	Mansions of Madness: Second Edition
9	Pandemic Legacy: Season 1	Alien Frontiers	The Castles of Burgundy	Concordia
10	Dominion	Can't Stop	Tales of the Arabian Nights	Tichu
11	Orléans	Pandemic	Grand Austria Hotel	The Quacks of Quedlinburg
12	Clank! In! Space!: A Deck-Building Adventure	Terraforming Mars	Concordia	Teotihuacan: City of Gods
13	7 Wonders	Hive	Great Western Trail	Spirit Island
14	Twilight Struggle	Carcassonne	Age of Empires III: The Age of Discovery	Cascadia
15	Wingspan	Camel Up	Architects of the West Kingdom	The Castles of Burgundy
16	Azul	For Sale	Glen More II: Chronicles	Sagrada
17	Splendor	Memoir '44	Honey Buzz	Orléans
18	Dice Forge	Mage Knight Board Game	Alien Frontiers	Cryptid
19	Power Grid	Cosmic Encounter	Just One	Caverna: The Cave Farmers
20	Lost Ruins of Arnak	Glen More II: Chronicles	Fresco	Res Arcana
21	Terra Mystica	Hanabi	Power Grid	In the Hall of the Mountain King
22	Puerto Rico	Pandemic Legacy: Season 1	Rajas of the Ganges	Splendor
23	Haven	Blokus	Twice as Clever!	Between Two Cities
24	Notre Dame	The Quacks of Quedlinburg	Clinic: Deluxe Edition	Lords of Waterdeep
25	Nertz	San Juan	Maracaibo	Stone Age
26	Bezzerwizzer	Welcome To...	Dinosaur Island	Imperial Settlers: Empires of the North
27	Istanbul	The Resistance: Avalon	Dominant Species	Roll Player
28	Century: Spice Road	Roll Through the Ages: The Bronze Age	Automania	Raiders of the North Sea
29	Just One	Viticulture Essential Edition	Agricola	7 Wonders Duel
30	Codenames	Wavelength	Roll Player	Suburbia
31	Castles of Mad King Ludwig	Sheriff of Nottingham	Teotihuacan: City of Gods	Five Tribes
32	Mysterium	Skull	Orléans	Azul
33	Glen More II: Chronicles	Core Worlds	The Artemis Project	Power Grid
34	Stone Age	Dominion	Viticulture Essential Edition	Tzolk'in: The Mayan Calendar
35	Letters from Whitechapel	Stone Age	The Mind	Scythe
36	Sheriff of Nottingham	King of Tokyo	7 Wonders Duel	Kenjin
37	Lanterns: The Harvest Festival	Tobago	The Lord of the Rings: Journeys in Middle-Earth	Dice Miner
38	Pandemic	No Thanks!	For Sale	Castles of Mad King Ludwig
39	Patchwork	Lost Cities	Carcassonne	Potion Explosion
40	Photosynthesis	Just One	Android: Netrunner	High Society
41	Hardback	Exit: The Game – The Abandoned Cabin	Dominion	Tiny Towns
42	Res Arcana	Onitama	Roll Through the Ages: The Bronze Age	Space Base
43	San Juan (Second Edition)	Patchwork	Telestrations	Just One
44	The Resistance: Avalon	Diamonds: Second Edition	The 7th Continent	Sushi Go Party!
45	Onitama	7 Wonders	War of the Ring: Second Edition	Skull
46	Jaipur	The Grizzled	Wavelength	Kingdomino
47	Cribbage	Power Grid	Stone Age	Istanbul
48	Ticket to Ride: Europe	K2	Can't Stop	Glass Road
49	Hive	Battle Line	Catchphrase	Bruges
50	Santorini	Perudo	Last Will	The Crew: The Quest for Planet Nine

Top 50 Analysis Using Boardgamegeek Data¶

Imports and Helper Functions¶

The Top 50 Lists¶

Top 10¶

The Full Lists¶

Game Crossovers¶

Game Statistics¶

Comparing individual rankings against BGG rankings¶

Game weights¶

Player counts¶

Release Year¶

User Rating Counts¶

Game Mechanics Statistics¶

Game Families Statistics¶

Kickstarter¶

Component Family¶

Game Category Statistics¶

	person	name	year	users_rated
148	Seth	Catchphrase	1974	190
185	Zarin	Kenjin	2015	400
24	Darin	Nertz	1930	418
186	Zarin	Dice Miner	2021	599
22	Darin	Haven	2018	951
132	Seth	The Artemis Project	2019	1621
123	Seth	Clinic: Deluxe Edition	2020	1662
116	Seth	Honey Buzz	2020	2182
127	Seth	Automania	2015	2304
40	Darin	Hardback	2018	2801